Tunnel intelligent ventilation control method and system based on Internet of Things, electronic equipment and storage medium

Abstract

The invention discloses a tunnel intelligent ventilation control method and system based on the Internet of Things, electronic equipment and a storage medium, and the method comprises the steps: collecting environment parameters in a tunnel, and determining a difference distribution condition and a local ventilation demand of a local environment region in the tunnel; based on the difference distribution condition, dividing the tunnel space into a plurality of independent control units, and performing feature extraction on the environmental parameters of each independent control unit to obtain a ventilation demand priority; on the basis of the ventilation demand priority, a regulation and control rule base based on a fuzzy control algorithm is constructed, and an air volume distribution scheme of each independent control unit is determined in combination with the local ventilation demand; based on the air volume distribution scheme, the independent control units are controlled to conduct ventilation, airflow interaction information of the adjacent independent control units is collected, and an airflow dynamic balance adjustment scheme is generated based on the airflow interaction information; and dynamically correcting the air volume of each independent control unit based on the airflow dynamic balance adjustment scheme to complete tunnel ventilation control.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of tunnel engineering and artificial intelligence control intersection, and particularly relates to a tunnel intelligent ventilation control method and system based on Internet of Things, an electronic device and a storage medium. BACKGROUND

[0002] Tunnel ventilation control, as an important field of transportation infrastructure safety and operation management, is directly related to air quality, energy efficiency and driving safety in tunnels, and its research has strategic value that cannot be ignored. With the acceleration of urbanization and the expansion of tunnel construction scale, how to improve the adaptability and efficiency of the ventilation system through intelligent means has become a key issue to ensure the safety of tunnel operation.

[0003] However, the current tunnel ventilation control method often faces the problem of insufficient system integrity when dealing with complex environmental changes. Many solutions lack precise identification of differentiated needs in different areas inside the tunnel, resulting in uneven distribution of ventilation resources, with some areas possibly having insufficient ventilation while others wasting resources. This mode of global unified regulation is difficult to adapt to the dynamic changes of environmental parameters in long tunnels and the special needs of local areas, thereby affecting the overall system's operating efficiency and response speed.

[0004] Under this background, the field of tunnel ventilation control faces significant technical challenges. The core problem lies in how to effectively segment the internal space of the tunnel and independently regulate different areas according to their environmental characteristics. Since long tunnels usually have complex structures and functional characteristics, a simple overall ventilation strategy cannot meet the specific needs of local areas, and the lack of independent control ability in different areas further exacerbates the imbalance of resource allocation. In addition, this independent control also needs to establish a coordination mechanism between different areas to avoid the decline of ventilation effect caused by air flow interference. These two factors are closely related: independent control in different areas is the foundation, and coordination between different areas is an important condition to ensure the stable operation of the overall system.

[0005] Therefore, how to achieve independent ventilation control based on spatial segmentation in long tunnels and coordinate the dynamic balance of air flow between different areas through networking technology has become a key problem that needs to be solved in this research. SUMMARY

[0006] To solve the above technical problems, the present application provides a tunnel intelligent ventilation control method based on Internet of Things, comprising the following steps:

[0007] Collecting environmental parameters inside the tunnel to determine the differentiated distribution of local environmental areas in the tunnel and local ventilation needs;

[0008] Based on the difference distribution, the tunnel space is divided into several independent control units, and the environmental parameters of each independent control unit are extracted to obtain the ventilation demand priority of each independent control unit.

[0009] Based on the ventilation demand priority, a regulation rule library based on fuzzy control algorithm is constructed, and the local ventilation demand is combined to determine the air volume distribution scheme of each independent control unit.

[0010] Based on the air volume distribution scheme, the independent control units are controlled to ventilate, and the airflow interaction information of adjacent independent control units is collected, and the airflow dynamic balance adjustment scheme is generated based on the airflow interaction information.

[0011] Based on the airflow dynamic balance adjustment scheme, the air volume of each independent control unit is dynamically corrected to complete the tunnel ventilation control.

[0012] Preferably, the method for obtaining the difference distribution comprises:

[0013] The sensor equipment is deployed through a distributed network, and the environmental parameters of each region in the tunnel are collected to obtain a preliminary environmental data set, the environmental parameters including temperature parameters, humidity parameters and air quality parameters.

[0014] The preliminary environmental data set is denoised and outlier removed to obtain a cleaned environmental data set.

[0015] The cleaned environmental data set is analyzed by time series analysis to identify the dynamic change trend of each region and determine the fluctuation characteristics of each region parameter.

[0016] When the fluctuation characteristics exceed a first preset threshold, the cleaned environmental data set of the corresponding region is weighted to obtain weighted feature data.

[0017] Based on the weighted feature data, the difference of local environmental demand is analyzed based on the spatial distribution of each region in the tunnel to obtain the difference distribution.

[0018] Preferably, the method for obtaining the ventilation demand priority comprises:

[0019] A partitioning model of the tunnel is constructed, and the tunnel space is segmented based on the difference distribution to divide the tunnel space into several independent control units.

[0020] The environmental parameters of each independent control unit are extracted to obtain the environmental characteristics of each independent control unit, and the key environmental parameters are extracted from the environmental characteristics.

[0021] When the key environmental parameter exceeds a second preset threshold, the corresponding key environmental parameter is weighted and calculated, and the emergency degree of the ventilation demand is judged.

[0022] Based on the judgment result of the emergency degree, the ventilation demands of each independent control unit are sorted to obtain the ventilation demand priority.

[0023] Preferably, the method for obtaining the air volume distribution scheme comprises:

[0024] A regulation rule base based on a fuzzy control algorithm is constructed, and in the regulation rule base, a corresponding control strategy is matched based on the ventilation demand priority, so as to obtain the equipment operation parameters of each independent control unit under the control strategy.

[0025] Based on the equipment operation parameters, in combination with the constraint conditions of air volume distribution, the air volume distribution proportion of each independent control unit is calculated to obtain a preliminary air volume distribution scheme.

[0026] If the preliminary air volume distribution scheme does not meet the local ventilation demand, the distribution proportion is adjusted through iteration to regenerate an air volume distribution scheme, so as to obtain the final air volume distribution scheme.

[0027] Preferably, the method for obtaining the air flow dynamic balance adjustment demand comprises:

[0028] Air flow interaction information of adjacent independent control units is collected, the air flow interaction information is characterized, and the influence degree of the extracted features is classified by using a support vector machine to obtain key features of air flow interference.

[0029] Based on the key features of air flow interference, the adjacent independent control units affected are prioritized to obtain a unit combination that needs to be adjusted.

[0030] The key features of air flow interference are analyzed, and adjustment parameters of dynamic balance are calculated to obtain the air flow dynamic balance adjustment scheme.

[0031] The application also provides a tunnel intelligent ventilation control system based on the Internet of Things, which applies the above method and comprises a data acquisition module, a priority calculation module, an air volume distribution module, a scheme adjustment module and a dynamic adjustment module.

[0032] The data acquisition module is used to collect environmental parameters inside the tunnel, determine the difference distribution of local environmental areas in the tunnel and the local ventilation demand.

[0033] The priority calculation module divides the tunnel space into a plurality of independent control units based on the difference distribution, extracts features of the environmental parameters of each independent control unit, and obtains ventilation demand priorities of the independent control units;

[0034] The air volume distribution module constructs a regulation and control rule library based on a fuzzy control algorithm based on the ventilation demand priorities, and determines an air volume distribution scheme of each independent control unit in combination with the local ventilation demand;

[0035] The scheme adjustment module controls the independent control units to ventilate based on the air volume distribution scheme, collects airflow interaction information of adjacent independent control units, generates an airflow dynamic balance adjustment scheme based on the airflow interaction information;

[0036] The dynamic adjustment module dynamically corrects the air volume of each independent control unit based on the airflow dynamic balance adjustment scheme, and completes tunnel ventilation control.

[0037] The application also provides an electronic device, characterized by comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the tunnel intelligent ventilation control method based on the Internet of Things when executing the program.

[0038] The application also provides a computer readable storage medium, characterized by storing a computer program, wherein the computer program is executed to implement the tunnel intelligent ventilation control method based on the Internet of Things.

[0039] Compared with the prior art, the application has the following beneficial effects:

[0040] The application collects environmental parameters of different areas in the tunnel in real time by deploying a distributed sensor network, divides the tunnel space into a plurality of independent control units by establishing a partition division model, extracts features of environmental data of each unit to obtain ventilation demand priorities. A regulation and control rule library is constructed based on a fuzzy control algorithm, the regional environmental difference and the local demand identification result are combined to determine the ventilation equipment operation parameters of each unit and determine the air volume distribution scheme. The airflow interaction between adjacent units is analyzed through networking technology to obtain the adjustment demand of airflow dynamic balance. The partition coordination mechanism is called to dynamically correct the ventilation parameters of adjacent units, and the coordination parameters are optimized and the operation instructions of the ventilation equipment of each unit are updated. The application realizes accurate perception, partition control and dynamic coordination of the tunnel environment, effectively improves the ventilation efficiency and air quality management level. BRIEF DESCRIPTION OF DRAWINGS

[0041] In order to more clearly illustrate the technical solutions of the present application, the drawings required for use in the embodiments will be briefly introduced as follows. Obviously, the drawings described in the following embodiments are only some of the embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor on the basis of these drawings.

[0042] Figure 1 The method flow of the embodiment of the present application is intended to be;

[0043] Figure 2 The structural schematic diagram of the electronic device of the embodiment of the present application is intended to be.

[0044] Legend of reference signs:

[0045] 1010, processor; 1020, memory; 1030, input / output interface; 1040, communication interface; 1050, bus. DETAILED DESCRIPTION

[0046] The technical solutions in the embodiments of the present application will be described clearly and completely in combination with the drawings in the embodiments of the present application. Obviously, the described embodiments are only some of the embodiments of the present application, but not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor fall within the scope of protection of the present application.

[0047] It should be noted that, unless otherwise defined, the technical terms or scientific terms used in the embodiments should be understood as the general meaning understood by those skilled in the art to which the present disclosure belongs. The terms "first", "second", and similar terms used in the embodiments do not represent any order, number, or importance, but are only used to distinguish different components. The terms "include" or "contain" and similar terms mean that the elements or objects before the terms cover the elements or objects listed after the terms and their equivalents, and do not exclude other elements or objects. The terms "connect" or "connected" and similar terms are not limited to physical or mechanical connections, but can include electrical connections, whether direct or indirect. The terms "up", "down", "left", "right", and the like are only used to represent relative positional relationships, and when the absolute positions of the described objects change, the relative positional relationships may also change accordingly.

[0048] Embodiment one

[0049] In the present embodiment, as shown in Figure 1 A tunnel intelligent ventilation control method based on Internet of Things, comprising the following steps:

[0050] S1. Collect the environmental parameters inside the tunnel, determine the difference distribution of the local environmental area in the tunnel and the local ventilation demand.

[0051] The method for obtaining the difference distribution includes: deploying sensor devices through a distributed network, collecting environmental parameters for each area in the tunnel to obtain a preliminary environmental data set, the environmental parameters including temperature parameters, humidity parameters and air quality parameters; denoising and removing outliers from the preliminary environmental data set to obtain a cleaned environmental data set; using time series analysis on the cleaned environmental data set to identify the dynamic change trend of each area and determine the fluctuation characteristics of the parameters of each area; if the fluctuation characteristics exceed a first preset threshold, performing weighted processing on the cleaned environmental data set of the corresponding area to obtain weighted feature data; based on the weighted feature data, analyzing the difference in local environmental demand in combination with the spatial distribution of each area in the tunnel to obtain the difference distribution.

[0052] In this embodiment, in the scenario of tunnel environment monitoring, by deploying sensor devices through a distributed network, real-time collection of temperature, humidity and air quality in each area in the tunnel can be realized. Specifically, a sensor node is deployed every 100 meters along the line, and each node collects data once an hour, including temperature parameters, humidity parameters and air quality parameters, to obtain a preliminary environmental data set. Then, the preliminary environmental data set is denoised and outliers are removed to obtain a cleaned environmental data set. For the cleaned environmental data set, a time series analysis method can be applied to identify the dynamic change trend of the parameters of each area. Specifically, taking the temperature parameter as an example, it is found through analysis that the temperature fluctuation in the area near the tunnel entrance is large, with a daily change amplitude of 5 degrees, while the fluctuation in the middle area is small, only 1 degree. This fluctuation characteristic reflects the difference in regional environmental stability. Then, in the first preset threshold, the temperature fluctuation threshold is set to 3 degrees. By comparing the numerical values, it is found that the temperature fluctuation in the entrance area exceeds the threshold and needs to be weighted to increase the data weight to obtain weighted feature data. Finally, based on the weighted feature data, the difference in local environmental demand is analyzed in combination with the spatial distribution information of the tunnel. By constructing a difference distribution map, the distribution difference of the environmental parameters of each area can be visualized to obtain the difference distribution.

[0053] S2. Based on the difference distribution, the tunnel space is divided into a plurality of independent control units, and the environmental parameters of each independent control unit are extracted to obtain the ventilation demand priority of each independent control unit.

[0054] The method for obtaining the ventilation demand priority comprises: constructing a zoning model of the tunnel, segmenting the tunnel space based on the difference distribution, and dividing the tunnel space into a plurality of independent control units; extracting the environmental characteristics of each independent control unit based on the environmental parameters of each independent control unit, and extracting the key environmental parameters from the environmental characteristics; if the key environmental parameters exceed the second preset threshold, performing weighted calculation on the corresponding key environmental parameters, and judging the emergency degree of the ventilation demand; based on the judgment result of the emergency degree, sorting the ventilation demands of each independent control unit to obtain the ventilation demand priority.

[0055] In the embodiment, the zoning model of the tunnel is constructed, the tunnel space is segmented based on the difference distribution, and the tunnel space is divided into a plurality of independent control units. The zoning model can reasonably determine the boundary of each unit based on the geological conditions, traffic flow distribution and other factors in the tunnel, to ensure that the division result meets the actual demand. Then, the environmental characteristics of each independent control unit are extracted based on the environmental parameters of each independent control unit, and the key environmental parameters of each unit are analyzed. Then, the key environmental parameters and the second preset threshold are compared. Taking temperature as an example, it is found through analysis that the temperature near the middle region of the tunnel is 30 degrees, and the temperature threshold in the second preset threshold is 28 degrees. It is determined that the temperature is abnormal, and the environmental data of the unit is weighted calculated to judge the emergency degree of the ventilation demand. In the evaluation of the ventilation demand priority, the priority evaluation model is constructed, and then the priority of each unit is calculated according to the emergency degree to obtain the ventilation demand priority.

[0056] S3. Based on the ventilation demand priority, a regulation rule library based on the fuzzy control algorithm is constructed, and the air volume distribution scheme of each independent control unit is determined based on the local ventilation demand.

[0057] The method for obtaining the air volume distribution scheme comprises: constructing a regulation rule library based on the fuzzy control algorithm, matching the corresponding control strategy in the regulation rule library based on the ventilation demand priority, obtaining the equipment operation parameters of each independent control unit under the control strategy; based on the equipment operation parameters, combining the constraint conditions of air volume distribution, calculating the air volume distribution proportion of each independent control unit, and obtaining the preliminary air volume distribution scheme; if the preliminary air volume distribution scheme does not meet the local ventilation demand, the distribution proportion is adjusted through iteration to regenerate the air volume distribution scheme, and the final air volume distribution scheme is obtained.

[0058] In the embodiment, when the priority level is classified by using the fuzzy control algorithm, the fuzzy logic can be introduced to process the uncertainty of the environmental parameters. The fuzzy control method divides the input variables such as the air quality index and the traffic flow into fuzzy sets of "high", "medium", and "low", and judges the priority level of each independent control unit. Specifically, if the air quality of unit one is evaluated as "high risk" and the traffic flow is "high", the priority level of unit one is determined as the highest, and if the indexes of unit three are all "medium", the priority level of unit three is lower. When the fuzzy control rule base is matched with the priority level sorting result, the rules can be designed in advance, such as "if the priority level is high, the equipment operating power is increased to 80%". Specifically, if unit one matches the high priority level rule, the initial equipment operating parameter of unit one can be set as 5 meters per second, and the initial equipment operating parameter of unit two is 2 meters per second. If it is found that the local demand of unit one is still not met, the weight factor in the rule base is adjusted, such as increasing the weight of air quality from 0.5 to 0.7, and the operating parameter is recalculated. Finally, the wind speed can be adjusted to 6 meters per second. When the air volume distribution ratio is calculated, the constraint conditions of air volume distribution are combined, such as the total air volume upper limit of 10,000 cubic meters per hour. Unit one can be allocated 4,000 cubic meters, and unit two can be allocated 2,000 cubic meters. If the air quality of unit one in the initial scheme is still not up to standard, the ratio of unit one is increased to 4,500 cubic meters through iterative adjustment, and the final air volume distribution scheme is obtained.

[0059] S4. Based on the air volume distribution scheme, the independent control units are controlled to ventilate, and the airflow interaction information of adjacent independent control units is collected. Based on the airflow interaction information, an airflow dynamic balance adjustment scheme is generated.

[0060] The method for obtaining the airflow dynamic balance adjustment requirement comprises: collecting airflow interaction information of adjacent independent control units, extracting features of the airflow interaction information, classifying the influence degree of the extracted features by using a support vector machine, obtaining key features of airflow interference, sorting the adjacent independent control units affected by the key features of airflow interference, obtaining a unit combination that needs to be adjusted, analyzing the key features of airflow interference, calculating the adjustment parameters of dynamic balance, and obtaining an airflow dynamic balance adjustment scheme.

[0061] In this embodiment, first, the airflow interaction information of adjacent independent control units is collected, and the collected airflow interaction information is feature extracted. By analyzing the change trend of airflow velocity, the deviation angle of airflow direction, and the fluctuation amplitude of flow, etc., the core features reflecting the degree of airflow interference are extracted, providing accurate data basis for subsequent analysis and adjustment. The influence degree of the extracted features is classified by using support vector machine to determine the key features of airflow interference. If the change of airflow velocity has more significant influence on the ventilation effect, it will be classified as a key feature so as to focus on it in subsequent adjustment. Based on the key features of airflow interference, the affected adjacent independent control units are prioritized to obtain the unit combination that needs to be adjusted. Through the analysis of the key features, it is determined which airflow interaction between the independent control units has a greater impact on the overall ventilation effect, and these unit combinations are sorted according to the size of the influence degree. For example, if the airflow interference between two units leads to a serious decline in ventilation effect, it will be placed in the front row of priority adjustment so that the problem can be solved in time and the normal operation of the ventilation system can be restored in the subsequent adjustment process. Finally, according to the specific situation of the key features such as airflow velocity, direction and flow deviation, combined with the operating parameters and constraint conditions of the ventilation system, the parameter values that need to be adjusted such as the speed of the fan, the opening of the air valve, etc. are calculated through mathematical modeling and optimization algorithm to obtain the airflow dynamic balance adjustment scheme.

[0062] S5. Based on the airflow dynamic balance adjustment scheme, the air volume of each independent control unit is dynamically corrected to complete the tunnel ventilation control.

[0063] It should be noted that the method of the embodiment can be executed by a single device, such as a computer or a server, etc. The method of the embodiment can also be applied in a distributed scenario, and completed by multiple devices cooperating with each other. In this distributed scenario, one of the multiple devices can only execute one or more steps in the method of the embodiment, and the multiple devices can interact with each other to complete the method.

[0064] It should be noted that some embodiments of the present disclosure have been described above. Other embodiments are within the scope of the appended claims. In some cases, it should be understood that the size of the serial number of each step in the above-mentioned embodiments does not mean the order of execution. The execution order of each process should be determined according to its function and inherent logic, and should not constitute any limitation on the implementation process of the embodiment of the present application. The actions or steps recited in the claims can be executed in an order different from that in the above-mentioned embodiments and still achieve the desired results. In addition, the processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multi-task processing and parallel processing are also possible or can be advantageous.

[0065] Embodiment Two

[0066] In this embodiment, a tunnel intelligent ventilation control system based on Internet of Things comprises a data acquisition module, a priority calculation module, a wind volume distribution module, a scheme adjustment module and a dynamic adjustment module.

[0067] The data acquisition module is configured to acquire environmental parameters inside the tunnel, determine the difference distribution of local environmental areas in the tunnel and local ventilation demand; the priority calculation module is configured to divide the tunnel space into a plurality of independent control units based on the difference distribution, extract features of the environmental parameters of each independent control unit, and obtain ventilation demand priorities of the independent control units; the wind volume distribution module is configured to construct a regulation and control rule library based on a fuzzy control algorithm based on the ventilation demand priorities, and determine a wind volume distribution scheme of each independent control unit in combination with the local ventilation demand; the scheme adjustment module is configured to control the independent control units to ventilate based on the wind volume distribution scheme, acquire airflow interaction information of adjacent independent control units, and generate an airflow dynamic balance adjustment scheme based on the airflow interaction information; and the dynamic adjustment module is configured to dynamically correct the wind volume of each independent control unit based on the airflow dynamic balance adjustment scheme, and complete tunnel ventilation control.

[0068] The system of the above embodiment is configured to implement the tunnel intelligent ventilation control method based on Internet of Things of any of the above embodiments, and has the beneficial effects of the corresponding method embodiment, which will not be described herein.

[0069] It should be noted that the tunnel intelligent ventilation control system based on Internet of Things is embodied in the form of functional units. The term "module" herein can be implemented by software and / or hardware, and is not limited specifically.

[0070] For example, the "module" can be a software program, a hardware circuit or a combination of the two that implements the above functions. The hardware circuit can include an application specific integrated circuit (ASIC), an electronic circuit, a processor (such as a shared processor, a dedicated processor or a group processor, etc.) and a memory for executing one or more software or firmware programs, and other suitable components that integrate logic circuits and / or support the described functions.

[0071] Embodiment Three

[0072] Based on the same inventive concept, corresponding to the method of any of the above embodiments, the embodiment also provides an electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor executes the program to implement the tunnel intelligent ventilation control method based on Internet of Things of any of the above embodiments.

[0073] Figure 2 A more specific electronic device hardware structure diagram provided by the embodiment is shown, which can include: a processor 1010, a memory 1020, an input / output interface 1030, a communication interface 1040, and a bus 1050. The processor 1010, the memory 1020, the input / output interface 1030, and the communication interface 1040 are connected to each other through the bus 1050 for internal communication.

[0074] The processor 1010 can be implemented by a general-purpose CPU (Central Processing Unit), a microprocessor, an ASIC (Application Specific Integrated Circuit), or one or more integrated circuits, etc., for executing related programs to implement the technical solutions provided by the embodiments of the present specification.

[0075] The memory 1020 can be implemented by a ROM (Read Only Memory), a RAM (Random Access Memory), a static storage device, a dynamic storage device, etc. The memory 1020 can store an operating system and other application programs, and when the technical solutions provided by the embodiments of the present specification are implemented by software or firmware, the related program codes are stored in the memory 1020 and called and executed by the processor 1010.

[0076] The input / output interface 1030 is used to connect input / output modules to realize information input and output. The input / output modules can be configured as components in the device (not shown in the figure) or externally connected to the device to provide corresponding functions. The input device can include a keyboard, a mouse, a touch screen, a microphone, various sensors, etc., and the output device can include a display, a speaker, a vibrator, an indicator light, etc.

[0077] The communication interface 1040 is used to connect a communication module (not shown in the figure) to realize the communication interaction between the device and other devices. The communication module can realize communication through a wired manner (such as USB (Universal Serial Bus), a network cable, etc.) or a wireless manner (such as a mobile network, WIFI (Wireless Fidelity), Bluetooth, etc.).

[0078] The bus 1050 includes a channel for transmitting information between various components (such as the processor 1010, the memory 1020, the input / output interface 1030, and the communication interface 1040) of the device.

[0079] It should be noted that although the above device only shows the processor 1010, the memory 1020, the input / output interface 1030, the communication interface 1040 and the bus 1050, in the specific implementation process, the device can also include other components necessary for normal operation. In addition, those skilled in the art can understand that the above device can also only contain components necessary for the implementation of the embodiments of the present specification, and does not necessarily contain all the components shown in the figure.

[0080] The system of the above embodiment is used to implement the corresponding tunnel intelligent ventilation control method based on Internet of Things in any of the above embodiments, and has the beneficial effects of the corresponding method embodiments, which are not described here.

[0081] Embodiment Four

[0082] Based on the same inventive concept, corresponding to the method of any of the above embodiments, the present embodiment also provides a non-transitory computer readable storage medium storing computer instructions for causing the computer to execute the tunnel intelligent ventilation control method based on Internet of Things as described in any of the above embodiments.

[0083] The computer readable medium of the present embodiment includes permanent and non-permanent, removable and non-removable media, which can be realized by any method or technology to store information. The information can be computer readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, compact disc read-only memory (CD-ROM), digital versatile disc (DVD) or other optical storage, magnetic cassette, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information that can be accessed by a computing device.

[0084] The computer instructions stored in the storage medium of the above embodiment are used to cause the computer to execute the tunnel intelligent ventilation control method based on Internet of Things as described in any of the above embodiments, and have the beneficial effects of the corresponding method embodiments, which are not described here.

[0085] Those skilled in the art should understand that the above discussion of any embodiment is only exemplary, and is not intended to mean that the scope of the disclosure (including the claims) is limited to these examples; the technical features in the above embodiments or different embodiments can also be combined, the steps can be implemented in any order, and there are many other changes of different aspects of the embodiments as described above, which are not provided in details for the sake of brevity.

[0086] In addition, to simplify the illustration and discussion, and so as not to make the embodiments difficult to understand, the well-known power / ground connections to integrated circuit (IC) chips and other components can or can not be shown in the provided drawings. Furthermore, devices can be shown in block diagram form in order to avoid obscuring the embodiments, and this also takes into account the fact that the details regarding the implementation of these block diagram devices are highly dependent on the platform to which the embodiments are to be implemented (i.e., these details should be well within the understanding of one of ordinary skill in the art). Where specific details are set forth in order to describe the embodiments, it should be apparent to those skilled in the art that the embodiments can be practiced without these specific details or with variations on the specific details.

[0087] Although the present application has been described in conjunction with the embodiments, many alternatives, modifications, and variations will be apparent to those skilled in the art in light of the foregoing description. For example, other memory architectures (e.g., dynamic RAM (DRAM)) can use the embodiments discussed.

[0088] Accordingly, the units of the examples described in the embodiments can be implemented in electronic hardware, or in a combination of computer software and electronic hardware. Whether the functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. A person skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the present application.

[0089] The embodiments are intended to cover all such alternatives, modifications, and variations as falling within the broad scope of the appended claims. Accordingly, any and all departures from the embodiments are included within the scope of the present disclosure.

Claims

1. A tunnel intelligent ventilation control method based on the Internet of Things, characterized in that: The following steps are involved: Collect environmental parameters inside the tunnel to determine the differential distribution of local environmental areas and local ventilation requirements; Based on the differential distribution, the tunnel space is divided into a number of independent control units, and feature extraction is performed on the environmental parameters of each independent control unit to obtain the ventilation demand priority of each independent control unit; Based on the ventilation demand priority, a control rule library based on a fuzzy control algorithm is constructed, and the air volume distribution scheme of each independent control unit is determined in combination with the local ventilation demand; Based on the air volume distribution plan, controlling the independent control unit to perform ventilation, collecting airflow interaction information of adjacent independent control units, and generating an airflow dynamic balance adjustment plan based on the airflow interaction information; Based on the airflow dynamic balance adjustment scheme, the air volume of each independent control unit is dynamically corrected to complete the tunnel ventilation control.

2. The tunnel intelligent ventilation control method based on the Internet of Things according to claim 1 is characterized in that: The method for obtaining the difference distribution includes: Deploy sensor devices through a distributed network to collect the environmental parameters in each area of ​​the tunnel to obtain a preliminary environmental data set, wherein the environmental parameters include: temperature parameters, humidity parameters, and air quality parameters; Denoising and removing outliers on the preliminary environmental data set to obtain a cleaned environmental data set; Analyzing the post-cleaning environmental data set using time series to identify dynamic change trends in each region and determine fluctuation characteristics of parameters in each region; When the fluctuation characteristic exceeds a first preset threshold, weighted processing is performed on the post-cleaning environment data set of the corresponding area to obtain weighted characteristic data; Based on the weighted characteristic data and in combination with the spatial distribution of each area in the tunnel, the differences in local environmental requirements are analyzed to obtain the differential distribution.

3. The tunnel intelligent ventilation control method based on the Internet of Things according to claim 1 is characterized in that: The method for obtaining the ventilation demand priority includes: Constructing a tunnel partitioning model, segmenting the tunnel space based on the differential distribution, and dividing the tunnel space into a number of independent control units; Performing feature extraction on the environmental parameters of each of the independent control units to obtain environmental features of each of the independent control units, and extracting key environmental parameters from the environmental features; If the key environmental parameter exceeds a second preset threshold, weighted calculation is performed on the corresponding key environmental parameter to determine the urgency of ventilation demand; Based on the judgment result of the urgency, the ventilation requirements of the independent control units are sorted to obtain the ventilation requirement priority.

4. The tunnel intelligent ventilation control method based on the Internet of Things according to claim 1 is characterized in that: The method for obtaining the air volume distribution scheme includes: Constructing a control rule base based on a fuzzy control algorithm, matching corresponding control strategies based on the ventilation demand priority in the control rule base, and obtaining equipment operating parameters of each independent control unit under the control strategy; Based on the equipment operating parameters and the constraints of air volume distribution, the air volume distribution ratio of each independent control unit is calculated to obtain a preliminary air volume distribution plan; When the preliminary air volume distribution plan does not meet the local ventilation demand, the air volume distribution plan is regenerated by iteratively adjusting the distribution ratio to obtain the final air volume distribution plan.

5. The tunnel intelligent ventilation control method based on the Internet of Things according to claim 1 is characterized in that: The method for obtaining the airflow dynamic balance adjustment requirement includes: Collecting airflow interaction information between adjacent independent control units, performing feature extraction on the airflow interaction information, and classifying the influence of the extracted features using a support vector machine to obtain key features of the airflow interference; Prioritizing the affected adjacent independent control units based on the key features of the airflow disturbance to obtain a unit combination that needs to be adjusted; The key features of the airflow disturbance are analyzed, and the adjustment parameters of the dynamic balance are calculated to obtain the airflow dynamic balance adjustment scheme.

6. An Internet of Things-based intelligent tunnel ventilation control system, wherein the system applies the method according to any one of claims 1 to 5, characterized in that: include: Data acquisition module, priority calculation module, air volume distribution module, scheme adjustment module and dynamic adjustment module; The data acquisition module is used to collect environmental parameters inside the tunnel and determine the differential distribution of local environmental areas in the tunnel and local ventilation requirements; The priority calculation module divides the tunnel space into a number of independent control units based on the differential distribution, extracts features of the environmental parameters of each independent control unit, and obtains the ventilation demand priority of each independent control unit; The air volume distribution module constructs a control rule library based on the fuzzy control algorithm based on the ventilation demand priority, and determines the air volume distribution plan of each independent control unit in combination with the local ventilation demand; The scheme adjustment module controls the independent control units to perform ventilation based on the air volume distribution scheme, collects airflow interaction information of adjacent independent control units, and generates an airflow dynamic balance adjustment scheme based on the airflow interaction information; The dynamic adjustment module dynamically corrects the air volume of each independent control unit based on the airflow dynamic balance adjustment scheme to complete tunnel ventilation control.

7. An electronic device, characterized in that: The invention comprises a memory, a processor and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the tunnel intelligent ventilation control method based on the Internet of Things as claimed in any one of claims 1 to 5 is implemented.

8. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a computer program, and when the computer program is executed, the tunnel intelligent ventilation control method based on the Internet of Things as described in any one of claims 1 to 5 is implemented.

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